An agent-based framework for collaborative data mining optimization

Liu, Xiong; Tang, Kaizhi; Buhrman, John R.; Cheng, Huaining

doi:10.1109/cts.2010.5478500

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…We integrate the Drupal based database with analytical software tools for model building and management. To build NEI models in the scope of NEI modeling, we apply IAI’s internal data mining engine, ABMiner,5,6 to the NEI data for model building. ABMiner provides an optimization engine which exploits meta-learning to search for the data mining models with best performance and efficiency.…”

Section: Methodsmentioning

confidence: 99%

“…Representative algorithms include nearest neighbor algorithms, tree algorithms, and support vector machines. Second, we reused the meta-optimization strategy6 to select algorithms and tune parameters.…”

Section: Methodsmentioning

confidence: 99%

“…(3) Optimized data mining process. NEIMiner is supported by ABMiner,5,6 Intelligent Automation, Inc.’s (IAI) internal data mining tool for model building. ABMiner provides an optimization engine in the meta-learning level to exploit and search data mining models with best performance and efficiency among a wide range of data mining algorithms and their corresponding parameters.…”

Section: Introductionmentioning

confidence: 99%

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NEIMiner: nanomaterial environmental impact data miner

Tang¹,

Liu²,

Harper³

et al. 2013

IJN

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As more engineered nanomaterials (eNM) are developed for a wide range of applications, it is crucial to minimize any unintended environmental impacts resulting from the application of eNM. To realize this vision, industry and policymakers must base risk management decisions on sound scientific information about the environmental fate of eNM, their availability to receptor organisms (eg, uptake), and any resultant biological effects (eg, toxicity). To address this critical need, we developed a model-driven, data mining system called NEIMiner, to study nanomaterial environmental impact (NEI). NEIMiner consists of four components: NEI modeling framework, data integration, data management and access, and model building. The NEI modeling framework defines the scope of NEI modeling and the strategy of integrating NEI models to form a layered, comprehensive predictability. The data integration layer brings together heterogeneous data sources related to NEI via automatic web services and web scraping technologies. The data management and access layer reuses and extends a popular content management system (CMS), Drupal, and consists of modules that model the complex data structure for NEI-related bibliography and characterization data. The model building layer provides an advanced analysis capability for NEI data. Together, these components provide significant value to the process of aggregating and analyzing large-scale distributed NEI data. A prototype of the NEIMiner system is available at http://neiminer.i-a-i.com/.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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NEIMiner: nanomaterial environmental impact data miner

Tang¹,

Liu²,

Harper³

et al. 2013

IJN

Self Cite

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“…These features of RF uncover the interactions between genes in the absence of main effect. The algorithm is implemented in a number of open source software packages, such as R [13], Rapid miner [14], Weka [15] and Willows packages [16]. RF can be very suitable for handling large p-value problems.…”

Section: Machine Learningmentioning

confidence: 99%

A Machine Learning Based Approach for Detecting Non-Deterministic Tests and Its Analysis in Mobile Application Testing

Bhojan

Ramyachitra²,

Vivekanandan³

et al. 2018

ijarcs

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Abstract:Hundreds of different mobile devices are on the market, produced by different vendors, and with different software features and hardware components. Mobile applications, while running on different devices, may behave differently due to variations in the hardware or O.S. components. Since mobile applications are expected to be deployed and executed on diverse mobile platforms, they must be validated on different mobile platforms and devices. Due to the peculiarities of mobile application development, there is a need for a quality assurance approach that focuses on its challenges. Moreover, mobile test executions take long time because all the tests were executed on different environments and developers had to create complex tear down procedures. Such procedures were lengthy and far from perfect, leading to unpredictable failures. Regression testing is a crucial part of Mobile app development and it checks that software changes do not break existing functionality. In every regression test execution, the final results are expected either always pass or always fail for the same code. But, in real time project release cycles, some of the tests will be non-deterministic, in other words called flaky tests. It reduces the importance of regression testing cycle and it is very difficult to trust on these results. These results significantly reduced the trust in the tests and thus undermined the whole mobile app test automation effort. We trained machine learning classifiers separately on each test result dataset and compared performance across datasets. The proposed model predicts result types as Non-Deterministic orDeterministic tests from the regression suite results executed in various release cycles.

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